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Material innovation plays a very important role in technological progress and industrial development. Traditional experimental exploration and numerical simulation often require considerable time and resources. A new approach is urgently needed to accelerate the discovery and exploration of new materials. Machine learning can greatly reduce computational costs, shorten the development cycle, and improve computational accuracy. It has become one of the most promising research approaches in the process of novel material screening and material property prediction. In recent years, machine learning has been widely used in many fields of research, such as superconductivity, thermoelectrics, photovoltaics, catalysis, and high-entropy alloys. In this review, the basic principles of machine learning are briefly outlined. Several commonly used algorithms in machine learning models and their primary applications are then introduced. The research progress of machine learning in predicting material properties and guiding material synthesis is discussed. Finally, a future outlook on machine learning in the materials science field is presented. [ABSTRACT FROM AUTHOR]

Due to a growing number of environmental issues, including global warming, water scarcity, and fossil fuel depletion, the topic of modern materials in energy is becoming crucial for our civilization. The technological advancements that have been observed bring many innovations that significantly impact how energy can be generated, stored, and distributed. Moreover, new opportunities have emerged in energy and materials engineering due to the increasing computational capability of current data processing systems. Methods that are highly demanding, time-consuming, and difficult to apply may now be considered when developing complete and sophisticated models in many areas of science and technology. Combining computational methods and AI algorithms allows for multi-threaded analyses solving advanced and interdisciplinary problems. Therefore, knowledge and experience in this subject, as well as the investigation of new, more efficient, and environmentally friendly solutions, currently represent one of the main directions of scientific research. The Special Issue "Advances in Materials: Modelling Challenges and Technological Progress for Green Engineering and Sustainable Development" aims to bring together research on material advances, focusing on modelling challenges and technological progress (mainly for green engineering and sustainable development). Original research studies, review articles, and short communications are welcome, especially those focusing on (but not limited to) artificial intelligence, other computational methods, and state-of-the-art technological concepts related to the listed keywords within energy and materials engineering. [ABSTRACT FROM AUTHOR]

Zirconia-based materials are widely used in dentistry due to their biocompatibility and suitable mechanical and tribological behavior. Although commonly processed by subtractive manufacturing (SM), alternative techniques are being explored to reduce material waste, energy consumption and production time. 3D printing has received increasing interest for this purpose. This systematic review intends to gather information on the state of the art of additive manufacturing (AM) of zirconia-based materials for dental applications. As far as the authors know, this is the first time that a comparative analysis of these materials' properties has been performed. It was performed following the PRISMA guidelines and using PubMed, Scopus and Web of Science databases to select studies that met the defined criteria without restrictions on publication year. Stereolithography (SLA) and digital light processing (DLP) were the techniques most focused on in the literature and the ones that led to most promising outcomes. However, other techniques, such as robocasting (RC) and material jetting (MJ), have also led to good results. In all cases, the main concerns are centered on dimensional accuracy, resolution, and insufficient mechanical strength of the pieces. Despite the struggles inherent to the different 3D printing techniques, the commitment to adapt materials, procedures and workflows to these digital technologies is remarkable. Overall, the research on this topic can be seen as a disruptive technological progress with a wide range of application possibilities. [ABSTRACT FROM AUTHOR]

Various designs of furnaces for melting alloys are used in the foundry industry. Regardless of their design, they have one common detail, which is the lining of their interiors with refractory materials. This component in the design of a metal-melting furnace has a very important task—to protect the rest of the furnace assemblies from thermal and mechanical damage. Continuous technical progress and the quality requirements of casting production produce increasingly higher demands for refractory materials in connection with their development as well. The article presents the results of an innovative method of vibratory compaction of refractory material (high-alumina aluminosilicate) using reduced pressure. The analysis presents a comparative study of two methods used for forming refractory materials, i.e., the application of the mentioned innovative method and the classical (standard) method of compaction by vibration only. The effects of the introduced modification in the manufacture of ceramic shapes were evaluated by means of the material's resistance to thermal shock, linear expansion, and dimensional change due to firing, apparent density, open porosity, and apparent specific gravity, determination of total pore volume and pore size distribution by mercury porosimetry, and slag resistance. The tests performed indicate that the procedure of lowering the pressure during the vibratory compaction of the refractory material creates a more homogeneous structure with a smaller number and size of pores. This makes it possible to improve most of the parameters that determine the quality of the refractories used for the linings of the foundry furnace. [ABSTRACT FROM AUTHOR]

Technological progress in masonry structures has resulted in the creation of competitive solutions, which force the need for an ever deeper recognition of this type of structure. Masonry is a composite with heterogeneous strength properties. Therefore, the most appropriate way to accurately describe the behavior of the masonry structure under the influence of the working load are experimental research and their statistical and probabilistic analysis. This article presents a series of experimental tests carried out on real masonry structures. The results of the experiments were subjected to static evaluation, determining the most important parameter in the probabilistic analysis—the coefficient of variability of strength. The variability obtained in the experimental studies was used to determine the safety of the structure in the probabilistic method. Achieved values of coefficients of variation and safety coefficients proved to be satisfactory and adequate to the emerging technological progress in the production and embedding of masonry components. [ABSTRACT FROM AUTHOR]

The impact of nano-based technologies in endodontics for the identification and treatment of various dental infections is showing fast progress. Studies show that nanoparticles could serve as useful agents with many beneficial results and continue to be promising in the field of endodontics. To ensure progress and improvements on novel nanomaterials in relation to their physicochemical and biological properties, nano-identification methods for the detection and evaluation of diseases need to be further highlighted. This study aims to review the current technological progress and recent research outcomes as well as possible prospective applications of nano-based technologies in endodontics. A comprehensive literature survey has been carried out on the utilizations of nanomaterials and nano-characterization techniques in endodontics. The current status and recent applications in endodontics are discussed with illustrative examples. The results have shown that the progress and improved accuracy of nano-identification techniques enabled a better characterization, evaluation and selection of appropriate treatment plans for endodontics-related diseases. The results have been inspiring for further clinical investigations. Nano-endodontics is still a developing field with a strong potential for revolutions of novel materials and techniques in the diagnosis and treatment of dental diseases. Further improvements in nanoparticles properties will pave the way for the development of many beneficial endodontic therapeutic agents. The future looks encouraging for sustainable products and testing methods for clinical endodontic applications. [ABSTRACT FROM AUTHOR]

Topology optimization holds great potential to achieve the best performance for various fluid-related applications like aircraft components and microfluidic mixers. This paper reviews comprehensively the technical progress of this field over the last decade from the viewpoint of structural expression. The density-based approach has been widely adopted to design structures due to its simple concept, ease of implementation, and robustness. Different designs using such a pointwise method for systems under Stokes, laminar Navier–Stokes, turbulent, non-Newtonian, and steady-state/unsteady-state fluid flows are reviewed and discussed in depth. Both isogeometric analysis and the moving morphable components/voids methods will demonstrate their advantages regarding integration with computer-aided design. The moving morphable components/voids method also significantly reduces computing costs. From the viewpoint of boundary smoothness, we are most concerned about whether smoother boundaries can reduce objective functions such as energy dissipation even lower. Therefore, this work also concentrates on level set and spline expression methods. Furthermore, we identify isogeometric analysis and machine learning in shaping the field's future. In addition, the review highlights the following two challenges: achieving accurate fluid model construction and the relatively limited experimental validation of fluid topology optimization outcomes. [ABSTRACT FROM AUTHOR]

More and more electrically conducting materials are required to sustain the technological progress of civilization. Faced with the performance limits of classical materials, the R&D community has put efforts into developing nanomaterials, which can offer sufficiently high operational parameters. In this work, single-walled carbon nanotubes (SWCNTs) were doped with polyethyleneimine (PEI) to create such material. The results show that it is most fruitful to combine these components at the synthesis stage of an SWCNT network from their dispersion. In this case, the electrical conductivity of the material is boosted from 249 ± 21 S/cm to 1301 ± 56 S/cm straightforwardly and effectively. [ABSTRACT FROM AUTHOR]